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update week35

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This commit is contained in:
Morten Hjorth-Jensen
2021-09-14 22:57:56 +02:00
parent 1f9e4412cb
commit 697aa73253
9 changed files with 153 additions and 208 deletions
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doc/pub/week35/html/._week35-bs000.html
+1 -1
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@@ -406,7 +406,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 6, 2021</h4></center> <!-- date -->
<center><h4>Sep 14, 2021</h4></center> <!-- date -->
<br>
<p>
doc/pub/week35/html/._week35-bs069.html
-4
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@@ -459,10 +459,6 @@ X[:,<span style="color: #666666">1</span>] <span style="color: #666666">=</span>
X[:,<span style="color: #666666">2</span>] <span style="color: #666666">=</span> x<span style="color: #666666">*</span>x
<span style="color: #408080; font-style: italic"># We split the data in test and training data</span>
X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(X, y, test_size<span style="color: #666666">=0.2</span>)
scaler <span style="color: #666666">=</span> StandardScaler()
scaler<span style="color: #666666">.</span>fit(X_train)
X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_train)
X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
<span style="color: #408080; font-style: italic"># matrix inversion to find beta</span>
OLSbeta <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linalg<span style="color: #666666">.</span>inv(X_train<span style="color: #666666">.</span>T <span style="color: #666666">@</span> X_train) <span style="color: #666666">@</span> X_train<span style="color: #666666">.</span>T <span style="color: #666666">@</span> y_train
doc/pub/week35/html/week35-bs.html
+1 -1
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@@ -406,7 +406,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 6, 2021</h4></center> <!-- date -->
<center><h4>Sep 14, 2021</h4></center> <!-- date -->
<br>
<p>
doc/pub/week35/html/week35-reveal.html
+1 -5
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@@ -148,7 +148,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>&nbsp;<br>
<center><h4>Sep 6, 2021</h4></center> <!-- date -->
<center><h4>Sep 14, 2021</h4></center> <!-- date -->
<br>
<p>
@@ -3149,10 +3149,6 @@ X[:,<span style="color: #B452CD">1</span>] = x
X[:,<span style="color: #B452CD">2</span>] = x*x
<span style="color: #228B22"># We split the data in test and training data</span>
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=<span style="color: #B452CD">0.2</span>)
scaler = StandardScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)
<span style="color: #228B22"># matrix inversion to find beta</span>
OLSbeta = np.linalg.inv(X_train.T @ X_train) @ X_train.T @ y_train
doc/pub/week35/html/week35-solarized.html
+1 -5
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@@ -325,7 +325,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 6, 2021</h4></center> <!-- date -->
<center><h4>Sep 14, 2021</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -3089,10 +3089,6 @@ X[:,<span style="color: #B452CD">1</span>] = x
X[:,<span style="color: #B452CD">2</span>] = x*x
<span style="color: #228B22"># We split the data in test and training data</span>
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=<span style="color: #B452CD">0.2</span>)
scaler = StandardScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)
<span style="color: #228B22"># matrix inversion to find beta</span>
OLSbeta = np.linalg.inv(X_train.T @ X_train) @ X_train.T @ y_train
doc/pub/week35/html/week35.html
+1 -5
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@@ -330,7 +330,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 6, 2021</h4></center> <!-- date -->
<center><h4>Sep 14, 2021</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -3094,10 +3094,6 @@ X[:,<span style="color: #666666">1</span>] <span style="color: #666666">=</span>
X[:,<span style="color: #666666">2</span>] <span style="color: #666666">=</span> x<span style="color: #666666">*</span>x
<span style="color: #408080; font-style: italic"># We split the data in test and training data</span>
X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(X, y, test_size<span style="color: #666666">=0.2</span>)
scaler <span style="color: #666666">=</span> StandardScaler()
scaler<span style="color: #666666">.</span>fit(X_train)
X_train_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_train)
X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
<span style="color: #408080; font-style: italic"># matrix inversion to find beta</span>
OLSbeta <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linalg<span style="color: #666666">.</span>inv(X_train<span style="color: #666666">.</span>T <span style="color: #666666">@</span> X_train) <span style="color: #666666">@</span> X_train<span style="color: #666666">.</span>T <span style="color: #666666">@</span> y_train
doc/pub/week35/ipynb/ipynb-week35-src.tar.gz
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doc/pub/week35/ipynb/week35.ipynb
+148 -183
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@@ -10,7 +10,7 @@
"<!-- Author: --> \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
"Date: **Sep 6, 2021**\n",
"Date: **Sep 14, 2021**\n",
"\n",
"Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -402,7 +402,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
@@ -953,7 +956,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# matrix inversion to find beta\n",
@@ -972,7 +978,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n",
@@ -989,7 +998,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"Masses['Eapprox'] = ytilde\n",
@@ -1019,7 +1031,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"def R2(y_data, y_model):\n",
@@ -1036,7 +1051,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"print(R2(Energies,ytilde))"
@@ -1052,7 +1070,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"def MSE(y_data,y_model):\n",
@@ -1072,7 +1093,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"def RelativeError(y_data,y_model):\n",
@@ -1107,7 +1131,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import os\n",
@@ -1160,7 +1187,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# equivalently in numpy\n",
@@ -1232,7 +1262,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -1252,7 +1285,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.datasets import load_boston\n",
@@ -1274,7 +1310,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
@@ -1292,7 +1331,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# check for missing values in all the columns\n",
@@ -1309,7 +1351,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# set the size of the figure\n",
@@ -1330,7 +1375,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# compute the pair wise correlation for all columns \n",
@@ -1350,7 +1398,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"plt.figure(figsize=(20, 5))\n",
@@ -1378,7 +1429,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"X = pd.DataFrame(np.c_[boston['LSTAT'], boston['RM']], columns = ['LSTAT','RM'])\n",
@@ -1395,7 +1449,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
@@ -1419,7 +1476,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
@@ -1458,7 +1518,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# plotting the y_test vs y_pred\n",
@@ -1586,7 +1649,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import sklearn.linear_model as skl\n",
@@ -1656,7 +1722,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"np.random.seed()\n",
@@ -1679,7 +1748,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
@@ -1730,7 +1802,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# Common imports\n",
@@ -2304,31 +2379,12 @@
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1. -1.]\n",
" [ 1. -1.]]\n",
"test U\n",
"[[0. 0.]\n",
" [0. 0.]]\n",
"test VT\n",
"[[0. 0.]\n",
" [0. 0.]]\n",
"[[-0.70710678 -0.70710678]\n",
" [-0.70710678 0.70710678]]\n",
"[2.00000000e+00 3.35470445e-17]\n",
"[[-0.70710678 0.70710678]\n",
" [ 0.70710678 0.70710678]]\n",
"[[-3.33066907e-16 4.44089210e-16]\n",
" [ 0.00000000e+00 2.22044605e-16]]\n"
]
}
],
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"# SVD inversion\n",
@@ -3126,20 +3182,12 @@
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.08839767027751376\n",
"3.8294285924714866\n",
"[[0.92932998 2.63805954]\n",
" [2.63805954 8.61477117]]\n"
]
}
],
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# Importing various packages\n",
"import numpy as np\n",
@@ -3168,20 +3216,12 @@
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0850713352585812\n",
"1.5846946541436007\n",
"[[1. 0.63837291]\n",
" [0.63837291 1. ]]\n"
]
}
],
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"n = 100\n",
@@ -3223,40 +3263,12 @@
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1.09669785 3.23795259]\n",
" [-0.22166894 -1.30593687]\n",
" [ 0.10192631 0.04245426]\n",
" [ 0.82011099 2.55486566]\n",
" [ 0.32105408 0.96706068]\n",
" [ 0.60361795 1.47703672]\n",
" [-1.87875598 -5.24764141]\n",
" [ 0.03658513 0.37688017]\n",
" [-0.57033315 -1.70980756]\n",
" [-0.30923424 -0.39286425]]\n",
" 0 1\n",
"0 1.096698 3.237953\n",
"1 -0.221669 -1.305937\n",
"2 0.101926 0.042454\n",
"3 0.820111 2.554866\n",
"4 0.321054 0.967061\n",
"5 0.603618 1.477037\n",
"6 -1.878756 -5.247641\n",
"7 0.036585 0.376880\n",
"8 -0.570333 -1.709808\n",
"9 -0.309234 -0.392864\n",
" 0 1\n",
"0 1.000000 0.990742\n",
"1 0.990742 1.000000\n"
]
}
],
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
@@ -3285,49 +3297,12 @@
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2 3 4 5 6 7 \\\n",
"0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"1 0.0 0.079955 0.083252 0.080480 0.081776 0.083055 0.071202 0.072287 \n",
"2 0.0 0.083252 0.087624 0.083966 0.085810 0.087616 0.074576 0.076060 \n",
"3 0.0 0.080480 0.083966 0.085125 0.086868 0.088629 0.077734 0.079241 \n",
"4 0.0 0.081776 0.085810 0.086868 0.089002 0.091153 0.079679 0.081504 \n",
"5 0.0 0.083055 0.087616 0.088629 0.091153 0.093695 0.081663 0.083808 \n",
"6 0.0 0.071202 0.074576 0.077734 0.079679 0.081663 0.072591 0.074289 \n",
"7 0.0 0.072287 0.076060 0.079241 0.081504 0.083808 0.074289 0.076255 \n",
"8 0.0 0.073485 0.077660 0.080883 0.083467 0.086097 0.076120 0.078358 \n",
"9 0.0 0.074811 0.079394 0.082672 0.085583 0.088544 0.078096 0.080610 \n",
"10 0.0 0.061639 0.064871 0.068789 0.070813 0.072887 0.065314 0.067084 \n",
"11 0.0 0.062699 0.066259 0.070225 0.072518 0.074865 0.066904 0.068904 \n",
"12 0.0 0.063878 0.067775 0.071800 0.074368 0.076991 0.068629 0.070864 \n",
"13 0.0 0.065183 0.069422 0.073519 0.076366 0.079274 0.070494 0.072970 \n",
"14 0.0 0.066615 0.071207 0.075386 0.078520 0.081718 0.072505 0.075226 \n",
"\n",
" 8 9 10 11 12 13 14 \n",
"0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"1 0.073485 0.074811 0.061639 0.062699 0.063878 0.065183 0.066615 \n",
"2 0.077660 0.079394 0.064871 0.066259 0.067775 0.069422 0.071207 \n",
"3 0.080883 0.082672 0.068789 0.070225 0.071800 0.073519 0.075386 \n",
"4 0.083467 0.085583 0.070813 0.072518 0.074368 0.076366 0.078520 \n",
"5 0.086097 0.088544 0.072887 0.074865 0.076991 0.079274 0.081718 \n",
"6 0.076120 0.078096 0.065314 0.066904 0.068629 0.070494 0.072505 \n",
"7 0.078358 0.080610 0.067084 0.068904 0.070864 0.072970 0.075226 \n",
"8 0.080737 0.083272 0.068979 0.071034 0.073233 0.075583 0.078091 \n",
"9 0.083272 0.086096 0.071010 0.073303 0.075747 0.078348 0.081114 \n",
"10 0.068979 0.071010 0.059527 0.061166 0.062930 0.064825 0.066855 \n",
"11 0.071034 0.073303 0.061166 0.063004 0.064972 0.067075 0.069319 \n",
"12 0.073233 0.075747 0.062930 0.064972 0.067148 0.069465 0.071929 \n",
"13 0.075583 0.078348 0.064825 0.067075 0.069465 0.072001 0.074691 \n",
"14 0.078091 0.081114 0.066855 0.069319 0.071929 0.074691 0.077614 \n"
]
}
],
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# Common imports\n",
"import numpy as np\n",
@@ -4111,7 +4086,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"x = np.random.rand(100)\n",
@@ -4133,7 +4111,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import os\n",
@@ -4166,10 +4147,6 @@
"X[:,2] = x*x\n",
"# We split the data in test and training data\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
"scaler = StandardScaler()\n",
"scaler.fit(X_train)\n",
"X_train_scaled = scaler.transform(X_train)\n",
"X_test_scaled = scaler.transform(X_test)\n",
"\n",
"# matrix inversion to find beta\n",
"OLSbeta = np.linalg.inv(X_train.T @ X_train) @ X_train.T @ y_train\n",
@@ -4328,7 +4305,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"from mpl_toolkits.mplot3d import Axes3D\n",
@@ -4446,7 +4426,10 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"def FrankeFunction(x,y):\n",
@@ -4504,25 +4487,7 @@
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
doc/src/week35/week35.do.txt
-4
View File
@@ -2571,10 +2571,6 @@ X[:,1] = x
X[:,2] = x*x
# We split the data in test and training data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
scaler = StandardScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)
# matrix inversion to find beta
OLSbeta = np.linalg.inv(X_train.T @ X_train) @ X_train.T @ y_train